Impairment covariance and combining weight updates during iterative turbo interference cancellation reception

ABSTRACT

In a receive node of a wireless network, an iterative multi-user multi-stage interference cancellation receiver is used. After each stage of interference cancellation, interference characteristics change. An adaptive strategy is used in which after each stage of interference cancellation, impairment covariance is parametrically updated and combining weights of the receiver are adapted to reflect the updated impairment covariance.

RELATED APPLICATION

This application may be related, at least in part, to U.S. patentapplication Ser. No. (To be assigned), Attorney Docket No. 2380-1733,entitled “FINGER PLACEMENT I MULTI-STAGE INTERFERENCE CANCELLATION”(companion application) filed Dec. 21, 2011, which is incorporatedherein by reference in its entirety. This application also claimspriority and benefit of U.S. provisional application 61/540,144 entitled“MULTI-STATE TURBO EQUALIZATION AND INTERFERENCE CANCELLATION RECEIVERFOR WIRELESS SYSTEMS” filed on Sep. 28, 2011, which is incorporatedherein by reference in its entirety.

TECHNICAL FIELD

Technical field of present disclosure relates to updating impairmentcovariance and combining weights during iterative turbo interferencecancellation reception.

BACKGROUND

In a turbo interference cancellation receiver, interference such asinter-symbol-interference (ISI), code-division multiplexing (CDM)interference, and spatial-multiplexing interference due to single-user(SU) or multi-user (MU) MIMO can be cancelled based on soft estimates ofthe interfering symbols. The soft symbol estimates are formed using thedecoder outputs, which describe the likelihood ratios of the bits thatare used to determine these interfering symbols. Each likelihood ratiocan be converted to bit probability (i.e., probability of bit havingvalue 0 or 1). After cancellation, the received signal is re-equalizedusing new combining weights, which reflect a new impairment covariancematrix due to interference cancellation. The equalized symbols aredemodulated and converted to bit soft values, which are used by thevarious decoders, one for each user, codeword or MIMO stream, to produceupdated bit likelihood ratios. This iterative process of cancellation,equalization, demodulation, and decoding is referred to as turbointerference cancellation (turbo-IC).

One key aspect of turbo-IC implementation is adapting the equalizerformulation to the residual impairment characteristics. In some radiobase stations (RBSs), despread-level equalization such as G-Rake orG-Rake+ is used. The received signal is descrambled and despread for asymbol of interest and for a number of finger delays. The multipledespread values are combined according to a set of combining weights,which is dependent on the impairment covariance matrix. In the G-Rakeapproach, an estimate of the code-averaged impairment covariance matrixis obtained by parametrically formulating a self-interference covariancematrix using the estimated own-signal propagation characteristics whileinterference from other interfering signals and thermal noise is modeledas additive white Gaussian noise (AWGN). In the G-Rake+ approach, anestimate of the code-averaged impairment covariance matrix can beobtained by observing the despread values on one or more unoccupiedcodes. Previous studies have confirmed that such a practical approachcaptures the overall interference characteristics more accurately andresults in good performance relative to a G-Rake+ receiver that hasperfect knowledge about the impairment covariance matrix. Anothercommonly used receiver in a CDMA system is Rake receiver which modelsoverall interference as AWGN.

Finger delays (or finger placement) and combining weights are twoimportant design parameters for a G-Rake+ equalizer. In a practicaliterative multi-stage interference-cancellation based multiuser detector(MUD), or turbo-IC receiver, interference characteristics can change asa portion of the interference is cancelled. It would thus be desirableto update covariance estimates and combining weights.

SUMMARY

A non-limiting aspect of the disclosed subject matter is directed to amethod performed in a receive node of a communication network to performa first stage processing a symbol of interest contained in a firstcomposite signal, and to perform a second stage processing the samesymbol of interest contained in a second composite signal. The firststage processing comprises determining a first stage impairmentcovariance estimate, determining one or more first stage combiningweights based on the first stage impairment covariance estimate,performing a first stage equalization of the first composite signalbased on the first stage combining weights to generate a first stageequalized signal, determining one or more first stage interfering symbolestimates based on the first stage equalized signal, and canceling thefirst stage interfering symbol estimates from the first composite signalto generate an interference-reduced version of the first compositesignal. The second stage processing comprises determining a second stageimpairment covariance estimate based on the first stage impairmentcovariance estimate and one or more previous stage interfering symbolestimates, determining one or more second stage combining weights basedon the second stage impairment covariance estimate, and performing asecond stage equalization of the second composite signal based on thesecond stage combining weights to generate a second stage equalizedsignal. The second composite signal is based on the interference-reducedversion of the first composite signal, and the previous stagecorresponds to the first stage processing or to a previous run of thesecond stage processing.

Another non-limiting aspect of the disclosed subject matter is directedto a receiver of a receive node of a communication network. The receivercomprises a plurality of chains, in which each chain is structured toprocess a symbol of interest contained in a first composite signal in afirst stage, and to process the same symbol of interest contained in asecond composite signal in a second stage. Each chain of the receivercomprises an equalizer, a demodulator, a signal regenerator, and aninterference canceller. In the first stage, the equalizer is structuredto determine a first stage impairment covariance estimate, to determineone or more first stage combining weights based on the first stageimpairment covariance estimate, and to perform a first stageequalization of the first composite signal based on the first stagecombining weights to generate a first stage equalized signal. Thedemodulator is structured to demodulate the first equalized signal togenerate a first stage demodulated data. The signal regenerator isstructured to determine one or more first stage interfering symbolestimates based on the first stage demodulated data. The interferencecanceller is structured to cancel the first stage interfering symbolestimates from the first composite signal to generate aninterference-reduced version of the first composite signal. In thesecond stage, the equalizer is structured to determine a second stageimpairment covariance estimate based on the first stage impairmentcovariance estimate and one or more previous stage interfering symbolestimates, to determine one or more second stage combining weights basedon the second stage impairment covariance estimate, and to perform asecond stage equalization of the second composite signal based on thesecond stage combining weights to generate a second stage equalizedsignal. The second composite signal is based on the interference-reducedversion of the first composite signal, and the previous stagecorresponds to the first stage processing or to a previous run of thesecond stage processing.

Yet another non-limiting aspect of the disclosed subject matter isdirected to a non-transitory computer storage medium which has storedtherein programming instructions. When a computer executes theprogramming instructions, the computer executes the method performed inthe receive node as described above.

DESCRIPTION OF THE DRAWINGS

The foregoing and other objects, features, and advantages of thedisclosed subject matter will be apparent from the following moreparticular description of preferred embodiments as illustrated in theaccompanying drawings in which reference characters refer to the sameparts throughout the various views. The drawings are not necessarily toscale.

FIG. 1 illustrates an example scenario of a wireless network in whichmobile terminals and base station communicate with each other;

FIG. 2 illustrates a simplified block diagram of a communication linkbetween a transmit node and a receive node;

FIGS. 3A and 3B illustrate example diagrams representing models of aWCDMA/HSPA uplink transmission and reception;

FIG. 4 illustrates an example embodiment of an iterative receiver;

FIGS. 5A and 5B illustrate example embodiments of a signal regenerator(with soft symbol demodulator);

FIGS. 6A and 6B illustrate flow charts of example processes toregenerate an estimated signal (based on soft outputs of decoder);

FIG. 7 illustrates an example embodiment of an equalizer adapted toperform a signal add-back process;

FIG. 8 illustrates another example embodiment of an iterative receiver;

FIGS. 9A and 9B illustrate example embodiments of a signal regenerator(pre-decoding signal estimate generation);

FIGS. 10A and 10B illustrate flow charts of example processes toregenerate an estimated signal (based on outputs of demodulator);

FIG. 11 illustrates a further example embodiment of an iterativereceiver;

FIGS. 12A and 12B illustrate example embodiments of a signal regenerator(with symbol modulator);

FIGS. 13A and 13B illustrate flow charts of example processes toregenerate an estimated signal (based on hard outputs of decoder);

FIG. 14 illustrates a flow chart of an example method for updatingimpairment covariance and combining weights;

FIG. 15 illustrates a flow chart of an example process to implement afirst stage processing of the method for updating impairment covarianceand combining weights;

FIG. 16 illustrates an example embodiment of a G-Rake equalizer;

FIGS. 17A and 17B illustrate example embodiments of adespreader/combiner of the G-Rake equalizer;

FIG. 18 illustrates a flow chart of an example process to perform afirst stage equalization;

FIG. 19 illustrates a flow chart of an example process to cancelinterferences;

FIGS. 20A, 20B and 20C illustrate flow charts of example processes toestimate interferences;

FIG. 21 illustrates a flow chart of an example process to implement asecond stage processing of the method for updating impairment covarianceand combining weights;

FIG. 22 illustrates a flow chart of an example process to determinesecond stage impairment covariance estimate;

FIG. 23 illustrates a flow chart of an example process to determinevariances; and

FIG. 24 illustrates a flow chart of an example process to perform asecond stage equalization.

DETAILED DESCRIPTION

For purposes of explanation and not limitation, specific details are setforth such as particular architectures, interfaces, techniques, and soon. However, it will be apparent to those skilled in the art that thetechnology described herein may be practiced in other embodiments thatdepart from these specific details. That is, those skilled in the artwill be able to devise various arrangements which, although notexplicitly described or shown herein, embody the principles of thedescribed technology.

In some instances, detailed descriptions of well-known devices,circuits, and methods are omitted so as not to obscure the descriptionwith unnecessary details. All statements herein reciting principles,aspects, embodiments and examples are intended to encompass bothstructural and functional equivalents. Additionally, it is intended thatsuch equivalents include both currently known equivalents as well asequivalents developed in the future, i.e., any elements developed thatperform same function, regardless of structure.

Thus, for example, it will be appreciated that block diagrams herein canrepresent conceptual views of illustrative circuitry embodyingprinciples of the technology. Similarly, it will be appreciated that anyflow charts, state transition diagrams, pseudo code, and the likerepresent various processes which may be substantially represented incomputer readable medium and executed by a computer or processor,whether or not such computer or processor is explicitly shown.

Functions of various elements including functional blocks labeled ordescribed as “processors” or “controllers” may be provided throughdedicated hardware as well as hardware capable of executing associatedsoftware. When provided by a processor, functions may be provided by asingle dedicated processor, by a single shared processor, or by aplurality of individual processors, some of which may be shared ordistributed. Moreover, explicit use of term “processor” or “controller”should not be construed to refer exclusively to hardware capable ofexecuting software, and may include, without limitation, digital signalprocessor (shortened to “DSP”) hardware, read only memory (shortened to“ROM”) for storing software, random access memory (shortened to RAM),and non-volatile storage.

In this document, 3GPP terminologies—e.g., WCDMA, HSPA—are used asexamples for explanation purposes. Note that the technology describedherein can be applied to non-3GPP standards. Thus, the scope of thisdisclosure is not limited to the set of 3GPP wireless network systemsand can encompass many domains of wireless network systems. Also, a basestation (e.g., RBS, NodeB, eNodeB, eNB, etc.) will be used as an exampleof a network node in which the described method can be performed.However, it should be noted that the disclosed subject matter isapplicable to any node, such as relay stations, that receive wirelesssignals. Also without loss of generality, mobile terminals (e.g., UE,mobile computer, PDA, etc.) will be used as examples of wirelessterminals that communicate with the base station.

FIG. 1 illustrates an example scenario of a wireless network 100 inwhich a mobile terminal 130 and a base station 110 (corresponding tocell 120) communicate with each other. In the downlink, the base station110 is a transmit node and the mobile terminal 130 is a receive node. Inuplink, the situation is reversed. For simplicity, one mobile terminal130 and one base station 110 are shown. However, this should not betaken to be limiting. The concepts discussed can be expanded and appliedto networks with multiple base stations and mobile terminals.

FIG. 2 is a simplified block diagram of a communication link between atransmit node 210 and a receive node 230. The transmit node 210 performsoperations on the data stream, which can be a stream of bits, totransmit a corresponding signal x through a channel 220. While it isrecognized that the signal x transmitted from the transmit node 210 iscarried by RF carriers, for the purposes of this discussion, equivalentbaseband signaling is assumed. Thus, it can be said that baseband signalx is transmitted from the transmit node 210 through the channel 220which can be dispersive, non-dispersive, frequency-selective, orfrequency-flat. The signal r received at the receive node 230 throughthe channel 220 is a composite of some version of the transmitted signalx and noise n. That is, the received signal r can be expressed asfollows:

r={circumflex over (x)}+n  (1)

where {circumflex over (x)} represents a version of the transmittedsignal x received at the receive node 230. The noise n can be viewed asincluding any unwanted signals including interferences (from othercells, mobile stations, thermal noise, etc.) as well as interferencesdescribed above.

The receive node 230 is structured to perform enhancement processing onthe received signal r to increase the effective SINR of thecommunication link between transmit node 210 and the receive node 230.Generally, enhancement processing can be viewed as amplifying thetransmitted signal x and/or reducing the noise n. The receive node 230reproduces the data (bit) stream originally supplied to the transmitnode 210.

As noted before, in an iterative multi-stage interference-cancellation,the interference characteristics can change as a portion of theinterference is cancelled. Interference may be characterized by itscorrelation function, or by the residual interference power levels, eachassociated with an interfering signal. It can be important to adapt theequalization weights according to the updated interferencecharacteristics within each detection stage. As such, impairmentcovariance update can play an important role.

However, the inventors are not aware of any solutions for impairmentcovariance update in a practical iterative multi-stage soft cancellationbased multiuser detector (MUD). It is also desirable for any MUD type ofsolution to consider soft ISI cancellation, which is an importantfeature of turbo-IC.

In U.S. Patent Publication 2010/0020854 ('854 Publication), which isincorporated herein in its entirety by reference, parametric updateapplied to a non-parametrically obtained overall impairment covarianceestimate is used to form a partial impairment covariance estimate for amultiple-symbol joint detector. The partial impairment covarianceestimate excludes contribution from a number of symbols which arejointly detected. The partial impairment covariance estimate is used ina pre-equalization stage to suppress interference. The solution in the'854 Publication however does not apply to an iterative multi-stageinterference cancellation receiver.

U.S. Patent Publication 2008/0130719 ('719 Publication), which isincorporated herein in its entirety by reference, also describes anapproach to modify a non-parametrically obtained overall impairmentcovariance estimate using channel estimates and hypothesized MIMOtransmission configurations. However, like the '854 Publication, the'719 Publication does not address adapting the parametric modificationto changing residual interference in an iterative multi-stageinterference cancellation receiver.

As indicated above, a key aspect of turbo-IC implementation is adaptingthe equalizer formulation to the residual impairment characteristicsduring each stage, and that a despread-level equalization such asG-Rake+ can be used in which the received signal is descrambled anddespread for a symbol of interest and for a number of finger placements.

Finger placement for a G-Rake receiver is described in U.S. Pat. No.6,683,924 which is herein incorporated by reference in its entirety.G-Rake fingers can include energy-collecting andinterference-suppressing fingers. The energy-collecting fingers can bedetermined by multipath delays, whereas the interference-suppressingfingers can be determined by the delays of the energy-collecting fingersas well as the delay differentials of the multipaths. Theinterference-suppressing fingers can be determined by impairmentcorrelations. A first set of fingers can be used to measure impairmentcorrelation. A delay can be chosen as an interference-suppressing fingerwhen the impairment correlation between such a delay and that of analready chosen finger (energy-collecting or interference-suppressing) ishigh.

Finger placements (or finger delays) and combining weights are importantdesign parameters for an equalizer such as the G-Rake or G-Rake+equalizer. Since the interference characteristics can change as aportion of the interference is cancelled in an interference cancellationstage, it would be desirable to adapt the finger placements and/orcombining weights to the different residual impairment characteristicsduring different stages of the turbo-IC receiver.

In one aspect of the disclosed subject matter, a total impairmentcovariance estimate is obtained during a first turbo-IC stage, beforeany soft symbol cancellation takes place. This total impairmentcovariance estimate can be based on unoccupied codes despread values. Ina subsequent turbo-IC stage, a parametric update is applied.

The parametric update can be formed based on the level of residualinterference and channel response associated with a first interferingsignal. The parametric update can be formed also based on the level ofresidual interference and channel response associated with a secondinterfering signal. The levels of residual interference of the first andsecond signals can be determined by the outputs of the decoders thatprocess the codewords associated with the first and second signals,respectively. The updated impairment covariance estimate can be used toform the G-Rake+ combining weights.

Note that one or more aspects described herein may be applied to anyother iterative, multi-stage interference cancellation (IC) schemes withG-Rake+ equalization, such as iterative hard or soft pre-decoding IC forwhich the regenerated signal for cancellation is based on symbolestimates from the demodulator instead of the channel decoder. Inaddition, one or more aspects can be applied to iterative, multi-stagehard post-decoding interference cancellation when the regenerated signalfor cancellation is based on symbol estimates from re-encoding thebinary decoded information bits after the decoded information bits passthe cyclic redundancy check (CRC).

For a discussion regarding the update strategy, a block diagramrepresenting a model of a WCDMA/HSPA uplink transmission and receptionillustrated in FIG. 3A is used. In this instance, the mobile terminal130 is the transmit node and the base station 110 is the receive node.This also should not be taken to be limiting. In the downlink direction,it is contemplated that some or all mobile terminals 130 may alsoperform signal enhancement processing to which one or more aspects ofthe disclosed subject matter are applicable.

The model illustrated in FIG. 3A may be viewed to as a being an instanceof the transmit node 210 shown in FIG. 2. In FIG. 3A, information bitsof a first signal (labeled “bits #1”) are encoded by an encoder 310 toproduce encoded bits according to a forward-error-correcting (FEC) code,which are modulated by a modulator 320 to produce transmitted symbols.For ease of reference, the information bits of the first signal (bits#1) will be referred to as the first information stream. Then it can besaid that the encoder 310 encodes the first information stream toproduce a first encoded information stream, which is modulated by themodulator 320 to produce a first symbol stream. The first symbol streamis mapped by a serial-to-parallel converter 330 to one or multiplechannelization codes. FIG. 3A illustrates a case of transmitting twochannelization codes for the first symbol stream. However, the number ofchannelization codes can be one or more than one.

FIG. 3A shows that modulation takes place before the serial-to-paralleloperation splits the symbol stream into multiple symbol streams formulti-code transmission. But as shown in FIG. 3B, an alternativeimplementation is to have the serial-to-parallel operation by theserial-to-parallel converter 335 directly follow the encoder 310 tosplit the single bit stream into multiple ones, each processed by amodulator 325 to produce a corresponding symbol stream. Each symbolstream is spread using one of the channelization codes.

In FIGS. 3A and 3B, the transmitted symbols in the first symbol streamare separately spread by spreaders 340 on each of the channelizationcodes to produce spread signals corresponding to the channelizationcodes, and an adder 350 sums the spread signals produced by thespreaders 340. The summed spread signals from the adder 350 arescrambled by a scrambler 360 to produce a first signal x₁ which istransmitted. In practice, other data and control channels are mapped onadditional channelization codes. But for the purposes of this discussionand without loss of generality, these signals are omitted.

The first transmitted signal x₁ is sent through a radio channel to thereceive node 230 (e.g., a base station). The channel may be dispersive.FIGS. 3A and 3B also show a second signal x₂, generated in a similarfashion to the first signal x₁ as being transmitted to the receive node230. The signals x₁ and x₂ can be transmitted from the same user, butvia different transmit antennas in the case of SU-MIMO (with the samescrambling code), or from different users in the case of multi-userscheduling or MU-MIMO (with different scrambling codes). It should benoted that there can be more than two signals generated and transmittedto the receive node 230 and the concepts discussed herein can begeneralized to any number of such generated signals. But for ease ofexplanation, only two are illustrated.

The base station receives a signal r, which includes some versions of x₁and x₂ (denoted respectively as {circumflex over (x)}₁ and {circumflexover (x)}₂) along with other signals (e.g., control channels, low-ratedata channels), and other impairments (other-cell interference, thermalnoise). That is, the signal r received at the base station can beexpressed as follows:

r={circumflex over (x)} ₁ +{circumflex over (x)} ₂ +n  (2)

Again, the noise signal n can be viewed as including any unwantedsignals including interferences. Just as an aside, the received signal rcan be generally expressed as follows:

$\begin{matrix}{r = {{\sum\limits_{k = 1}^{m}{\hat{x}}_{k}} + n}} & (3)\end{matrix}$

where m represents the number of signals transmitted to the receive node230.

A high-level architecture of an example turbo-IC receiver 400 capable ofrecovering the information bits from the first and second signalsgenerated in FIG. 3 is shown in FIG. 4. For brevity, the turbo-ICreceiver 400 will simply be referred to as the “receiver” 400. Thereceiver 400 comprises an antenna buffer 410 structured to store thereceived signal at the first stage or interference reduced version ofthe received signal at later stages, one or more equalizers 420structured to equalize signals from the antenna buffer 410, one or moredemodulators 430 structured to demodulate the equalized signals, one ormore decoders 440 structured to decode the demodulated signals, one ormore signal regenerators 450 structured to regenerate signals, one ormore user memories 460 structured to store the regenerated signalsand/or symbols of different stages, and an interference canceller 470structured to cancel interferences in each stage. The receiver 400 canalso include an equalizer 425, a demodulator 435 and a decoder 445 toprocess WCDMA/HSPA low-rate signals. These can be same or different fromthe equalizers 420, the demodulators 430 and the decoders 440. It shouldbe noted that actuality, these can be any signals that are not processedin an iterative manner. As such, they can be of any rate, not just lowrate. But for ease of reference, they are referred to as “HS low-ratesignal” in the Figures.

The receiver 400 can be viewed as being included in the receive node 230illustrated in FIG. 2. In the uplink transmission, the receiver 400 canbe a receiver of a base station, and in the downlink transmission, itcan be a receiver of a mobile terminal. As expressed in equation (2)above, the receive node 230 receives a signal r which is a combinationof signals as {circumflex over (x)}₁ and {circumflex over (x)}₂(versions of originally transmitted first and second signals x₁ and x₂)plus other signals and impairments or a noise signal n.

In FIG. 4, two chains of equalizer 420, demodulator 430, decoder 440,signal regenerator 450 and user memory 460 are shown. In other words,the two signals x₁ and x₂ are detected in parallel. Each chain processesthe received signal r for the signal of interest. For example, thesignal of interest for the top chain (also referred to as the firstchain) processes can be the first signal x₁, and the signal of interestfor the second (middle) chain can be the second signal x₂. While twochains are shown, this is not a limitation. The number of chains can beany number.

For each signal of interest, e.g., the first signal x₁, the equalizer420 equalizes the signal stored in the antenna buffer 410 (which can bethe received signal r or the interference reduced version of the signalof interest) to produce a stream of equalized symbols in that signal ofinterest. For example, the first chain equalizer 420 in FIG. 4 producesa stream of equalized symbols corresponding to the first symbol streamproduced by the top modulator 320 in FIG. 3A. Generally, the equalizedsymbols of the stream produced by the equalizer 420 can be viewed asestimates of the symbols in the symbol stream produced by acorresponding modulator 320. It can be said that from the perspective ofa particular symbol of interest, the equalizer 420 equalizes thatsymbol. The equalizer 420 also equalizes other symbols in the samesymbol stream (i.e., of the same signal) which can be sources of ownsignal interference. Symbol streams of other streams (i.e., of othersignals) which are other interference sources to the symbol of interestare also equalized.

The demodulator 430 demodulates the equalized symbol to produce ademodulated data. In one example, this can be a number of encoded bitsoft values corresponding to the symbol of interest. The decoder 440decodes the demodulated data to produce likelihood indicators. Forexample, this can be a number of bit log-likelihood ratios (LLR) foreach of the encoded bits. Other examples of the likelihood indicatorsinclude simple ratios and probability. It should be noted that any typeof likelihood indications can suffice. The likelihood indicators fromthe decoder 440 are used by the signal regenerators 450 to obtain anestimate of the signal transmitted from the transmit node 210.

In FIG. 4, the outputs of the signal regenerators 450 of the two chainscan be estimates of the first and second signals x₁ and x₂. Theinterference canceller 470 cancels the estimated signals from the totalreceived signal, and the cleaned-up version of the received signal canbe used in a subsequent stage of signal detection. In one aspect,interference canceller 470 reads the contents of the antenna buffer 410,cancels the interference, and writes the result back to the antennabuffer 410.

At any stage of interference cancellation, the interference canceller470 cancels interferences from other detected signals, e.g.,interferences of signals x₁ and x₂ from each other. Own signalinterferences such as ISI are also cancelled. However, differentinterfering signals can have different levels of cancellation. The levelof cancellation depends on the likelihood indicators such as the LLRs.If the LLRs have a high magnitude, indicating strong confidence, thelevel of cancellation is high. For example, the decoding of x₁ couldresult in a much stronger confidence (e.g., due to lower coding rate,higher received power, etc.) than that of x₂.

An example architecture of the signal regenerator 450 is illustrated inFIG. 5A. The signal regenerator 450 comprises a soft modulator 520, aserial-to-parallel converter 530, one or more spreaders 540, an adder550, a scrambler 560, and a channel filter 570. A flow chart of anexample process performed by the signal regenerator 450 in FIG. 5A isillustrated in FIG. 6A.

For each symbol of interest, the soft modulator 520 in step 610 forms asoft symbol based on the likelihood indicators (e.g., LLRs) output bythe decoder 440. The soft symbol can represent an estimate of the symbolof interest. The soft symbol can also represent an estimate of aninterfering symbol. In one aspect, the soft modulator 520 formulateseach soft symbol as a conditional mean based on the likelihoodindicators (e.g., the bit LLRs) output by the decoder 440. Theserial-to-parallel converter 530 maps the soft symbol into thechannelization codes in step 620. Again, the number of channelizationcodes can be one or greater than one. The soft symbol is spread byspreaders 540 on each of the channelization codes in step 630, and thespread signals are summed together by the adder 550 in step 640,scrambled by the scrambler 560 in step 650, and channel-filtered by thechannel filter 570 in step 660 to produce an estimate of the transmittedsignal, e.g., an estimate of the signal x₁ or x₂. Of course, it isrecognized that where there is only one channelization code, steps 620and 640 need not be performed.

An alternative example architecture of the signal regenerator 450 isillustrated in FIG. 5B. Compared to FIG. 5A, the order of soft symbolmodulation and serial-to-parallel processing are reversed. As seen, thesignal regenerator 450 in FIG. 5B comprises a serial-to-parallelconverter 535 and one or more soft modulators 525. Like FIG. 5A, thesignal regenerator in FIG. 5B also includes one or more spreaders 540,an adder 550, a scrambler 560, and a channel filter 570.

A flow chart of an example process performed by the signal regenerator450 in FIG. 5B is illustrated in FIG. 6B. For each symbol of interest,the serial-to-parallel converter 535 maps the likelihood indicators fromthe decoder 440 into the channelization codes in step 615, the number ofchannelization codes being one or greater than one. The soft modulators525 in step 625 form soft symbols based on the mapped likelihoodindicators. The soft symbols together can represent an estimate of thesymbol of interest or an estimate of an interfering symbol. Again, thesoft modulators 525 can formulate the correspondingly mapped softsymbols as conditional means. The remaining steps are similar to thesteps in FIG. 6A, and thus the details are not repeated. Of course, itis recognized that where there is only one channelization code, steps620 and 640 need not be performed.

Note that the architecture of the signal regenerators 450 illustrated inFIGS. 5A and 5B are respectively similar to the transmit node modelsillustrated in FIGS. 3A and 3B. This is logical since it is preferableto generate the estimate of the signal in a way same or similar to theway in which the originally transmitted signal is generated. In oneaspect, the correspondence between FIGS. 3A and 5A and between FIGS. 3Band 5B can be described as follows. The soft symbol modulators 520, 525generate streams of soft symbols that are estimates of the correspondingstreams of symbols generated by the modulators 320, 325. The softsymbols are used to regenerate an estimation of the signal of interestcontained in the received signal. From the perspective of each symbol ofinterest, the soft symbol modulators 520, 525 generate an estimate ofthat symbol of interest and generate estimates of interfering symbols inthe same symbol stream. Estimates of interfering symbols in differentsymbol streams are also generated.

The turbo-IC receiver architecture of FIG. 4 is advantageous as the sameregenerated signal can be used for other-signal cancellation as well asown ISI cancellation. However, this approach can result in anover-cancellation, where part of the desired signal is also cancelled(the non-ISI portion of the signal). The over-cancellation can becorrected through a signal add-back process performed by the equalizer420.

An example architecture of the equalizer 420 that can perform the signaladd-back process is illustrated in FIG. 7. In this figure, a G-Rake+equalizer is adapted to perform the signal add-back process, which canbe performed during the G-Rake+ equalization. As shown, the soft symbols is added back to form a fully equalized symbol ŝ. The scaling term g₀is determined to ensure that the fully equalized symbols ŝ is a MMSE andML symbol estimate. An example of soft ISI cancellation using thedecoder output LLRs is described in US Patent Publication 2007/0147481('481 Publication) incorporated by reference in its entirety herein.

A high-level architecture of another example turbo-IC receiver capableof recovering the information bits from the first and second signals isshown in FIG. 8. Note that the receiver 800 comprises components similaror identical to that of the receiver 400 such as the antenna buffer 410,equalizers 420, demodulators 430, user memories 460 and the interferencecanceller 470. The receiver 800 can perform iterative hard and/or softpre-decoding interference cancellation based on the output of thedemodulator 430. Thus, the decoder 440 need not be included in thereceiver 800 for pre-decoding cancellation.

Example architectures of the signal regenerator 850 are illustrated inFIGS. 9A and 9B. As seen, the signal regenerator 850 includes componentsto that of the signal regenerator 450 illustrated in FIG. 5A. Thus, thedetailed descriptions with regard to the similar components are notrepeated. The signal regenerators 450 and 850 in FIGS. 5A and 9A differin that the regenerator 850 includes a modulator 920 structured tooutput a symbol based on the demodulated bits output (in the case ofhard pre-decoding interference cancellation) or bit LLRs (in the case ofsoft pre-decoding interference cancellation) from the demodulator 430.The signal regenerators 450 and 850 in FIGS. 5B and 9B differ in thatthe regenerator 850 includes one or more modulators 925 structured tooutput a symbol based on the mapped (corresponding to channelizationcodes) demodulated bits (in the case of hard pre-decoding interferencecancellation) or bit LLRs (in the case of soft pre-decoding interferencecancellation) output from the serial-to-parallel converter 535.

Flow charts of example processes for signal regeneration generationperformed by the signal regenerators 850 of FIGS. 9A and 9B arerespectively illustrated in FIGS. 10A and 10B. In FIG. 10A, themodulator 920 in step 1010 forms the symbol estimate based on thedemodulated bits or bit LLRs output by the demodulator 430. Theremaining steps are similar to the steps in FIG. 6A. In FIG. 10B, theserial-to-parallel converter 535 maps the demodulated bits or bit LLRsfrom the demodulator 430 according to the channelization codes in step1015, and the modulators 925 form the estimates of the symbol forchannelization codes in step 1025. The remaining steps are similar tothe steps in FIG. 6B. Again, when there is only one channelization code,steps 620 and 640 need not be performed in both FIGS. 10A and 10B.

A high-level architecture of yet another example turbo-IC receivercapable of recovering the information bits from the first and secondsignals is shown in FIG. 11. In the receiver 1100, the decoder 1140 isassumed to output hard information bits instead of encoded bitlikelihood indicators, and signal regenerator 1150 can regenerate thesignal based on the hard information bits. Example high levelarchitectures of the signal regenerator 1150 are shown in FIGS. 12A and12B. Again, detailed descriptions of similar components are notrepeated.

For iterative hard post-decoding multi-layer or multi-user interferencecancellation, the regeneration of the signal can be based on hardinformation bits of the decoder output when the cyclic redundancy check(CRC) passes. As seen in FIGS. 12A and 12B, the signal regenerator 1150includes a CRC checker 1210 structured to check the CRC of the output ofthe decoder 1140 and a hard reencoder 1215 structured to encode theoutput of the decoder 1140 to generate reencoded bits. When the CRC doesnot pass, the estimated signal is neither regenerated nor cancelled asseen by the arrow exiting from top of the CRC checker 1210. When the CRCdoes pass, the signal can be regenerated by first reencoding thedetected hard information bits from the decoder 1140 and then formingthe estimated symbols based on the reencoded data. This is illustratedby the arrow from the reencoder 1215 entering the modulator 1220.

Flow charts of example processes for signal regeneration generationperformed by the signal regenerator 1150 are illustrated in FIGS. 13Aand 13B. As seen, in the CRC checker 1210 determines whether the CRCpasses in step 1310. In FIG. 13A, if the CRC passes, then in step 1320,the reencoder 1215 reencodes the de'coded hard information bits from thedecoder 1140, and in step 1330, the modulator 1220 forms the symbolestimate based on the reencoded bits. If the CRC does not pass,regeneration is not performed. The remaining steps are similar to thesteps of FIGS. 6A and 10A.

In FIG. 13B, if the CRC passes in step 1310, the reencoder 1215reencodes the decoded hard information bits from the decoder 1140, andin step 1325, the serial-to-parallel converter 535 maps the reencodedbits according to the channelization codes. Then in step 1335, themodulators 1225 form the estimates of the symbol for channelizationcodes based on the mapped reencoded bits. If the CRC does not pass,regeneration is not performed. The remaining steps are similar to thesteps of FIGS. 6B and 10B.

As mentioned previously, in an iterative multi-stageinterference-cancellation, the interference characteristics can changeas a portion of the interference is cancelled. In one aspect of thedisclosed subject matter, the impairment covariance and/or combiningweights are updated according to the new interference characteristicsafter interference cancellation. An example method for processingsignals is illustrated in FIG. 14. The impairment covariance and/orcombining weights are updated as the signals are processed. The method1400 can be performed in the receive node 230 of a communication network100 to process a symbol of interest carried in a received signal. Forexample, the symbol of interest can be a symbol carried in the firstsignal x₁.

Broadly, in the method 1400, the receive node 230 performs a first stageprocessing on the symbol of interest contained in a first compositesignal in step 1410. Subsequently, the receive node 230 performs asecond stage processing 1420 on the same symbol of interest contained ina second composite signal in step 1420. The first composite signal maybe assumed to be the received signal r. In the first stage processing1410, total impairment covariance affecting the symbol of interest isdetermined. After the first stage processing 1410, a cleaned-up signalis generated by canceling at least a portion of the interferences of thefirst composite signal. The cleaned-up signal can be viewed as aninterference-reduced version of the first composite signal.

As a result of interference cancellation in the first stage processing1410, the interference characteristics of the cleaned-up signal willlikely be different from the originally received signal. Thus, in thesecond stage processing 1420, the impairment covariance and/or combiningweights are adapted accordingly and the interference can be furthercanceled. This could result in a further cleaned-up signal. The furthercleaned-up signal is also an interference-reduced version of the firstcomposite signal.

In step 1430, the receive node 230 determines whether the processing ofthe symbol of interest can stop. This can be determined in a variety ofways such as reaching a predetermined level of interferencecancellation, reaching a predetermined number of iterations of thesecond stage processing 1420, reaching CRC check, reaching apredetermined level QoS parameters such as of SINR, BER, FER, and so on.If further processing is needed (“no” branch from 1430), the secondstage processing 1420 is performed again.

Note that each second stage processing 1420 can change the interferencecharacteristics. Thus, through each iteration of the second stageprocessing 1420, the impairment covariance and/or the combining weightsare readapted based on the changed interference characteristics, i.e.,based on the interference characteristics of the second composite signalinputted to the second stage processing 1420. As indicated in thecompanion application, processing delays can be determined as theinterference characteristics change, and the despreading and combiningof the signal can be performed based on the processing delays.

A more detailed example implementation of the method 1400 to updateimpairment covariance and combining weights is described as follows.Prior to the first stage processing 1410 being performed, nointerference cancellation has been done for the symbol of interest.Thus, impairment covariance can be estimated through using unoccupiedchannelization code despreading and averaging as explained in U.S.Publication 2008/0304554 ('554 Publication) which is hereby incorporatedin its entirety by reference. For the first signal x₁ in the first stageprocessing 1410, let C₁ be the impairment covariance matrix obtained forthe G-Rake+ equalizer 420 equalizing the first signal x₁ (e.g., the topequalizer 420 in FIG. 4). It can be shown that

C ₁ =E(1)C _(I,1)(1)+E(2)C _(I,1)(2)+R _(N,1),  (4)

where E(i) is the total symbol energy for signal i, C_(I,1)(i) is theimpairment covariance matrix contributed by signal i, and R_(N,1)accounts for contribution by signals that are not included in turbo-ICcancellation, plus noise.

In one aspect, the impairment covariance matrix C₁ of equation (4) is atotal impairment covariance (derived non-parametrically), or at least anestimate thereof, obtained before any interference cancellation hastaken place for the symbol of interest. Equation (4) is mainly toillustrate the composition of C₁. But it should be noted that whenunoccupied channelization code despreading and averaging is used asdescribed in the '554 Publication, C₁ can be obtained directly withoutknowing its composition, i.e., non-parametric estimation.

Note that from the perspective of the first signal x₁, C_(I,1)(1) is dueto self interference, whereas C_(I,1)(2) is due to other-signalinterference (e.g., due to the second signal x₂). The expression for theelement of C_(I,j)(i) corresponding to fingers having delays d₁ and d₂is given by

$\begin{matrix}{\left( {C_{I,j}(i)} \right)_{d_{1},d_{2}} = {\frac{1}{N}{\sum\limits_{i = 0}^{{L{(i)}} - 1}{\sum\limits_{q = 0}^{{L{(i)}} - 1}{{g_{i}(l)}{g_{i}^{*}(q)}{\chi \left( {i,j,{d_{1} - {\tau_{i}(l)}},{d_{2} - {\tau_{i}(q)}}} \right)}}}}}} & (5)\end{matrix}$

in which N is the spreading factor of signal j (which the equalizerunder consideration is associated with), L(i) is the number ofresolvable paths corresponding to signal i's propagation channel, andg_(i)(l) and τ_(i)(l) are the complex channel coefficient and delaycorresponding to the lth path, respectively. The function χ(i, j, t₁,t₂) is represented as follows:

$\begin{matrix}{{\chi \left( {i,j,t_{1},t_{2}} \right)} = {\sum\limits_{m = {- \infty}}^{\infty}{\left( {1 - {{\delta \left( {i - j} \right)}{\delta (m)}}} \right){R_{p}\left( {t_{1} - {mT}_{c}} \right)}{R_{p}^{*}\left( {t_{2} - {mT}_{c}} \right)}}}} & (6)\end{matrix}$

where R_(p)(τ) is the pulse shape autocorrelation function. In equation(6), the term 1−δ(i−j)δ(m) is used to exclude the contribution from m=0only in the case of self interference (i=j). This is due to the use oforthogonal spreading codes.

As mentioned earlier, the soft modulators 520, 525 (See FIGS. 5A, 5B)can use the likelihood indicators (e.g., the bit LLRs) output from thedecoder 440 to compute the conditional mean (soft symbol) for a symbolof interest. A detailed description can be found in US PatentPublication 2011/0222618 ('618 Publication) which is herein incorporatedit its entirety by reference. The level of interference cancellationdepends on the variance of a regenerated soft symbol. The variance ofthe symbol for signal j on the kth channelization code during the ithsymbol interval can be obtained by

σ _(s,j) ²(k,i)=E[|s _(j)(k,i)|² |I _(j)(k,i)]−|[s _(j)(k,i)|I_(j)(k,i)]|²,  (7)

where I_(j)(k,i) are the soft outputs from the decoder 440 whichindicate the LLRs of a number of encoded bits used to determine thesymbol s_(j)(k,i). The expression of equation (7) can also be referredto as the variance of the symbol s_(j)(k,i). Note that if the decoder440 outputs likelihood indicators I_(j)(k,i) that indicate completecertainty of bits having values 1 or 0, then the variance σ _(s,j)²(k,i)=0. In other words, signal j does not contribute to the impairmentcovariance matrix when complete certainty is indicated. On the otherhand, if I_(j)(k,i) bits indicate equal likelihood of value 1 or 0, thenσ _(s,j) ²(k,i)=1.

The variance can be further averaged over all the symbols (over k andi),

$\begin{matrix}{\sigma_{\overset{\_}{s},j}^{2} = {\frac{1}{KI}{\sum\limits_{k = 0}^{K - 1}{\sum\limits_{i = 0}^{I - 1}{{\sigma_{\overset{\_}{s},j}^{2}\left( {k,i} \right)}.}}}}} & (8)\end{matrix}$

In the second IC iteration, the impairment covariance matrix can beupdated based on the computed soft symbol variances. Let {tilde over(C)}₁ represent the impairment covariance matrix after interferencecancellation. In can be shown that {tilde over (C)}₁ becomes

{tilde over (C)} ₁ =E(1)σ _(s,1) ² C _(I,1)(1)+E(2)σ _(s,2) ² C_(I,1)(2)+R _(N,1).  (9)

{tilde over (C)}₁ can be obtained by parametrically updating theoriginal (total) impairment covariance matrix C₁ as seen in thefollowing equations.

{tilde over (C)} ₁ =C ₁ −ΔC ₁  (10)

ΔC ₁ =E(1)η(1)C _(I,1)(1)+E(2)η(2)C _(I,1)(2)  (11)

η(i)=1−σ _(s,i) ²  (12)

As indicated above, the original impairment covariance matrix C₁ can beobtained using the unoccupied channelization code despreading andaveraging.

In equations (10), (11) and (12), η(i) can be thought of as thecancellation efficiency from soft subtraction, which is determined bythe variance, or average power, of the interfering symbol estimates.Also E(i)η(i) can be thought of as the update factor. When theimpairment covariance matrix estimate is updated as indicated above, theupdated impairment covariance matrix {tilde over (C)}₁ can be used toform new G-Rake+ equalizer combining weights w₁={tilde over (C)}₁ ⁻¹h₁,where h₁ is the net response for the signal x₁.

The variance σ _(s,j) ² can be approximated by the residual interferencepower after cancellation. In this instance, the conditional mean s_(j)(k,i)=E[s_(j)(k,i)I_(j)(k,i)]|, which is output by the softmodulators 520, 525, can be used as estimated interfering symbols forcancellation. Thus, on average, the amount of interference power that iscancelled is

${P_{IC}(j)} = {\frac{1}{KI}{\sum\limits_{k = 0}^{K - 1}{\sum\limits_{i = 0}^{I - 1}{{{{\overset{\_}{s}}_{j}\left( {k,i} \right)}}^{2}.}}}}$

The residual power from signal j is thus E(j)(1−P_(IC)(j)). In thiscase, the impairment covariance matrix after IC becomes

{tilde over (C)} ₁ =E(1)(1−P _(IC)(1))C _(I,1)(1)+E(2))C _(I,1)(2)+R_(N,1)  (13)

Based on equations (9), (12) and (13), it is seen that the cancellationefficiency becomes η(i)=1−σ _(s,i) ²=P_(IC)(i).

In the signal regeneration and cancellation process, a number ofchannels can be included. In addition to data channels, control channelsand pilots can be regenerated and cancelled. These channels can beregenerated and cancelled separately, or together with the data channelsin one step. The general covariance estimate update principle describedabove is applicable also for control channels and pilots.

Note that the detailed example above describes post-decoding turbo-ICwith soft bit decisions for both cancellation and covariance matrixupdate. Post-decoding IC denotes a way in which the soft output of thedecoder 440 is used to regenerate an estimate of the received signal asillustrated in FIGS. 4, 5A, 5B, 6A and 6B, which is then subtracted inthe interference cancellation process. However, there are severaldifferent alternatives for cancellation and covariance updates, and theycan be applied differently for different types of symbols, such as datasymbols, control symbols and pilot symbols.

One alternative is to use the output of the demodulator 430 toregenerate the estimated signal as illustrated in FIGS. 8, 9A, 9B, 10Aand 10B. This is denoted as pre-decoding IC in which the properties ofthe FEC code are not necessarily used to improve the regeneration.However, the regeneration can be made sooner, without incurring adecoding processing delay. In this case, the likelihood indicatorsI_(j)(k,i) in equation (7) will correspond to the outputs from thedemodulator 430 instead of the decoder 440.

Another design choice is whether to use hard or soft bit decisions (ormore generally hard or soft symbol estimations) in the regenerationprocess as illustrated in 11, 12A, 12B, 13A and 13B. Hard bit decisionsare typically easier to implement, and if the decisions are correct, theperformance is good. However, if there are uncertainties in the bitvalues, soft bits and soft symbols are preferably used in signalregeneration as in FIGS. 4, 5A, 5B, 6A and 6B.

For the impairment covariance matrix update, there is a possibility touse either hard or soft bit decisions. Typically, if soft (or hard) bitsare used in the signal regeneration, also soft (or hard) bits arepreferably used for the impairment covariance update. But there can beexceptions. For example, even if hard decisions are used for signalregeneration due to hardware and software simplifications, one may wantto use soft bits in the impairment covariance matrix update since harddecisions can contain greater degree of errors, and hence, thecancellation may not be satisfactory.

Conversely, there can be instances where soft bits are used for signalregeneration, but hard decisions are used in the impairment covarianceupdate. For example, if the signal regeneration and the impairmentcovariance update are performed in parallel or separated in hardware orsoftware in such a way that the soft bits are not available when theimpairment covariance matrix is to be updated, hard bits can be used.

When hard bit decisions are used for the impairment covariance matrixupdate, there are several options for how to reflect the confidence inthese bits or symbols. In one aspect, completely correct detectiondecisions are assumed by assigning variance σ _(s,j) ²(k,i)=0 for thosesymbols. Another option for symbols with hard decisions is to still usesoft information as described above even though the soft values areconverted to hard decisions in the process that regenerates and cancelsthe signal. This can reflect some uncertainty in the correctness of thehard decisions.

In another aspect, post-decoding bits are used for the signalregeneration and the pre-decoding bits are used for the impairmentcovariance update, and vice versa. These design choices can enableefficient hardware and software implementation. There is also thepossibility to use an older or filtered estimate of the cancellationefficiency η(i) or the update factor E(i)η(i) when performing theimpairment covariance matrix update (see equations (10), (11) and (12)).

For coded channels such as data channels, post-decoding IC is typicallyused. However, pre-decoding IC is an alternative for the coded channels.For data symbols, soft bits are typically used for both signalregeneration and impairment covariance update.

For control channels that need to be correctly detected before theinterference cancellation can start, it is typical to assume that thecontrol bits have been correctly detected, so hard decisions are used.However, also here, soft bit decisions can be used for signalregeneration and/or impairment covariance update.

For pilot symbols hard bit decisions are preferably used, since nodecisions are needed because the bit values are known alreadybeforehand. In the signal regeneration, hard decisions can be used. Butfor the impairment covariance update, using soft bits can beadvantageous to reflect other uncertainties than incorrect bit decisionsin the interference cancellation, for example errors in the channelestimate used for signal regeneration.

The variances of the symbols from different control channels can beaveraged separately, and also separately from the data channels, using,e.g., equation (8). For example, let σ _(s,j,d) ², σ _(s,j,c) ², and σ_(s,j,p) ² be the averaged variances for the data, control, and pilot(or a different control) channels, respectively, and α_(d), α_(c), andα_(p) be their respective power allocation factors. The parametricupdate can be obtained by equations (10) and (11). However, thecancellation efficiency becomes

η(i)=α_(d)(1−σ _(s,j,d) ²)+α_(c)(1−σ _(s,j,c) ²)+α_(p)(1−σ _(s,j,p)²)  (14)

Thus far, channels that are not included in the IC processing (e.g.,some control channels and possible other low-rate channels from the sameuser or antenna) have been ignored. To account for this, let β be thefraction of power allocated to the channels that are not cancelled usingturbo-IC, and thus 1−β to the channels that are included in turbo-IC. Inthis case, the cancellation efficiency calculated according to equations(12) and (13) can be further scaled by the factor 1−β. In this case, theimpairment covariance matrix after interference cancellation becomes

{tilde over (C)} ₁ =E(1)(β(1)+(1−β(1))σ _(s,1) ²)C_(I,1)(1)+E(2)(β(2)+(1−β(2))σ _(s,2) ²)C _(I,1)(1)+R _(N,1)  (15)

Note that when β=0, equations (9) and (15) become identical. Regardless,the residual interference power level can be calculated using theknowledge of total signal power, power allocation to the channels thatare included in turbo-IC, and the decoder feedback.

The impairment covariance matrix contributed by signal i, C_(i,1)(i),can be calculated using equations (5) and (6). Note that χ(i, j, t₁, t₂)can be pre-computed and stored in a table for a number of (t₁, t₂)combinations. As a result, the updated impairment covariance matrix{tilde over (C)}₁ can be obtained by parametrically modifying theoriginal impairment covariance matrix C₁. The power levels E(i) or powerallocation factors β, α_(d), α_(c), and α_(p) can be estimated using acode power estimation method as described in the '719 publication.

An alternative approach to calculating the impairment covarianceestimate update {tilde over (C)}₁ is to use the regenerated transmitsignal. In an interference cancelling receiver, such as a turbo-ICreceiver, first a regenerated transmit signal including all data,control and pilot channels that will be cancelled is calculated. Thisregenerated transmit signal is filtered with an estimate of the channelto obtain a regenerated receive signal, which is then cancelled, forexample by subtracting it from the antenna buffer that stores thereceived signal. The update factor E(i)η(i) in equation (11) can then bedirectly estimated for each user i by setting it to the average varianceor power of the regenerated transmit signal for that user.

FIG. 15 illustrates a flow chart of an example process performed by thereceiver 400 to implement the first stage processing 1410 for the symbolof interest contained in the first composite signal. As seen, theequalizer 420 determines in step 1520 the first stage impairmentcovariance estimate.

FIG. 16 illustrates an example architecture of a G-Rake+ equalizer 420.As seen, the equalizer 420 includes a despreader/combiner 1610 thatincludes a plurality of fingers. The despreader/combiner 1610 isstructured to output despread and combined value for the symbol ofinterest. A delay timing determiner 1620 is structured to determine thedelays (finger placements) for each despread value, and a combiningweight calculator 1640 is structured to calculate the weight of eachdespread value. A channel estimator 1630 is structured to estimate thechannel and an impairment covariance estimator 1650 is structured toestimate the impairment covariance of the first composite signal.

Referring back to FIG. 15, the impairment covariance estimator 1650determines the first stage impairment covariance matrix in step 1520. Inone aspect, the impairment covariance estimator 1650 estimates the firststage impairment covariance matrix based on unoccupied channelizationcode despreading and averaging. Based on the first stage impairmentcovariance matrix, the combining weight calculator 1640 determines thefirst stage combining weights in step 1530.

In step 1540, the equalizer 420 performs a first stage equalization.FIG. 18 illustrates a flow chart of an example process to implement step1540. In step 1810, the delay timing determiner 1620 determines firststage processing delays. In step 1820, the despreader/combiner 1610despreads and combines the first composite signal based on the firststage combining weights. As a result, a first stage equalized signal isoutput. The first stage equalized signal can be viewed as a stream offirst stage equalized symbols. FIGS. 17A and 17B are example embodimentsof the despreader/combiner 1610. As seen, both embodiments of thedespreader/combiner 1610 can comprise a plurality of delays (fingers)1710, one or more correlators 1720, a plurality of multipliers 1730, andone or more adders 1740. The embodiments can also include anothermultiplier 1735 for performing desired signal add-back processingdescribed earlier.

The despreader/combiner 1610 embodiment illustrated in FIG. 17Adespreads the first composite signal and combines the despread valuesbased on the first combining weights provided by the combining weightcalculator 1640 to output the first stage equalized signal. Thedespreader/combiner 1610 embodiments illustrated in FIG. 17B combinesthe first composite signal based on the first stage processing delaysand the first combining weights, despreads the combined values, and thedespread values are output as the first stage equalized signal. In theseembodiments, the first combining weights can be combining weights ofenergy-collecting fingers only or combining weights for bothenergy-collecting and interference-suppression fingers.

Referring back to FIG. 15, the receiver 400 estimates one or more firststage interferences to the symbol of interest based on the first stageequalized signal in step 1550. The interferences to the symbol ofinterest can be due to interferences in signals corresponding to othersymbols being processed. This can be from own signal or from othersignals. Thus, to estimate the first stage interferences, the firststage interfering symbol estimates are determined.

In one aspect, the demodulator 430, the decoder 440 and the signalregenerator 450 can perform the step of estimating symbols including thesymbol of interest as well as interfering symbols as illustrated in FIG.20A. In this aspect, the first stage interferences are estimated basedon soft outputs of the decoder 440. In step 2010, the demodulator 430demodulates the first stage equalized signal and generates first stagedemodulated bit soft values corresponding to the symbol of interest. Instep 2020, the decoder 440 decodes the first stage demodulated bit softvalues to generate first stage likelihood indicators such as LLR,ratios, and so on. Then in step 2030, the signal regenerator 450determines the first stage estimates of the symbols in the signal ofinterest based on the first stage likelihood indicators generated by thedecoder 440. Note that the demodulation/estimation steps are repeatedfor all symbols carried by signal x_(i). In doing so, an estimate of thesymbol of interest is generated. But estimates of the interferingsymbols are also generated. That is, the first stage interfering symbolestimates are determined. The first stage interfering symbol estimatesgenerated by the signal regenerator 450 correspond to the symbolestimates output by the soft symbol modulators 520, 525 in FIGS. 5A and5B.

In another aspect, the demodulator 430 and the signal regenerator 850can perform the step 1540 as illustrated in FIG. 20B. In this aspect,the pre-decoding signal regeneration is performed based on the hard orsoft outputs of the demodulator 430. In step 2010, the demodulator 430demodulates the first stage equalized signal and generates first stagedemodulated bits (in case of hard pre-decoding IC) or first stagelikelihood indicators (in case of soft pre-decoding IC). In step 2032,the signal regenerator 850 determines the first stage estimate of thesymbol based on the first stage demodulated bits or likelihoodindicators. Again, the demodulation/estimation steps are repeated forall symbols carried by signal x_(i) to determine the first stageinterfering symbol estimates. The first stage interfering symbolestimates generated by the signal regenerator 850 correspond to thesymbol estimates output by the modulators 920, 925 in FIGS. 9A and 9B.

In yet another aspect, the demodulator 430, the decoder 1140 and thesignal regenerator 1150 can perform the step 1540 as illustrated in FIG.20C. In this aspect, the signal regeneration is performed based on hardoutputs of the decoder 1140. In step 2014, the demodulator 430demodulates the first stage equalized signal and generates first stagedemodulated bits corresponding to the symbol. In step 2024, the decoder1140 decodes the first stage demodulated bits to generate hard decodedinformation bits. In step 2026, assuming that the CRC check passes, thesignal regenerator 1150 reencodes the hard decoded information bits, andin step 2034, the signal regenerator 1150 determines the first stageestimate of the symbol based on the hard outputs. Here, thereencode/demodulation/estimation steps are repeated for all symbolscarried by signal x_(i) to determine the first stage interfering symbolestimates. The first stage interfering symbol estimates generated by thesignal regenerator 1150 correspond to the symbol estimates output by themodulators 1220, 1225 in FIGS. 12A and 12B.

Again referring back to FIG. 15, based on the first stage interferingsymbol estimates determined in step 1550, the interference canceller 470cancels the first stage signal estimate from the first composite signalto generate the interference-reduced version of the first compositesignal in step 1560. An example process to implement step 1560 isillustrated in FIG. 19. In step 1910, an estimate of the signal isdetermined based on the first stage interfering symbol estimates. Asindicated above, the demodulation/estimation steps are repeated for allsymbols carried by signal x_(i) to generate the first stage interferingsymbol estimates, which can correspond to the outputs of the soft symbolmodulators 520 and 525 in FIGS. 5A and 5B, to the outputs of themodulators 920 and 925 in FIGS. 9A and 9B, or to the outputs of themodulators 1220 and 1225 in FIGS. 12A and 12B. These interfering symbolestimates are spread, scrambled, and channel filtered to output theregenerated signal. The regenerated signal correspond to the outputs ofthe channel filter 570 in FIGS. 5A, 5B, 9A, 9B, 12A and 12B. Theregenerated signal output by the signal regenerator 450, 850, 1150 canbe viewed as an estimate of the signal of interest received at thereceive node 210. Flow charts of FIGS. 6A, 6B, 10A, 10B, 13A and 13B areexample implementations of the step 1910 of determining the signalestimate. Then in step 1920, the estimate of the signal of interest iscanceled from the composite signal by the interference canceller 470.

After the first stage processing 1410, the second stage processing 1420is performed to process the symbol of interest contained in the secondcomposite signal. The second composite signal can be theinterference-reduced version of the first composite signal. But recallthat the second stage processing 1420 can be performed more than once.Thus the second composite signal can be an interference-reduced versionof the second composite signal in a previous run of the second stageprocessing 1420.

FIG. 21 illustrates a flow chart of an example process performed by thereceiver 400 to implement the second stage processing 1420 for thesymbol of interest contained in the second composite signal. Recall fromabove that the level of interference cancellation can depend of thevariance of a regenerated symbol. Also recall that the variance can beapproximated by the residual interference power after cancellation.Further recall that the conditional mean can be used as an estimatedinterfering symbol for cancellation. As seen in equation (13), theimpairment covariance matrix after interference cancellation depends onthe residual power of the signals.

In step 2120, the equalizer 420, and the impairment covariance estimator1650 in particular, determines the second stage impairment covarianceestimate based on the first stage impairment covariance estimate and oneor more previous stage interfering symbol estimates. As seen inequations (10), (11) and (12), the first stage impairment covarianceestimate can be parametrically updated.

FIG. 22 illustrates a flow chart of an example process to perform thestep 2120. In step 2210, the equalizer 420 determines the variances ofinterfering symbols based on the previous stage interfering symbolestimates. Recall that the second stage processing 1420 can be performedonce or more than once. Thus, in this context, the previous stageinterfering symbol estimates are the first stage interfering symbolestimates (from step 1550) if the second stage processing 1420 has notbeen performed for the symbol of interest or the second stageinterfering symbol estimates (from step 2150) of a previous run of thesecond stage processing 1420. In step 2220, the equalizer 420 updatesthe first stage impairment covariance estimates based on the variances.In one aspect, the parametric update is performed as indicated by theabove described equations.

In an aspect, the variances of interfering symbols are obtained based onresidual interference power levels. As expressed in equation (13), theresidual interference power levels are power levels of corresponding tothe interfering symbols that remain after interference is canceled fromthe previous composite signal, which is one of the first compositesignal (if this is the first performance of the second stage processing)or the second composite signal (of a previous run of the second stageprocessing). Note that the residual interference power levels can beobtained through computing average power values of the previous stageinterfering symbol estimates of the interfering symbols.

FIG. 23 illustrates a flow chart of an example process to perform thestep 2210 to determine the variances of the interfering symbols. In step2310, the receiver 400, 800, 1100 determines residual powers based onthe previous stage interfering symbol estimates—the first stageinterfering symbol estimates (step 1550) or the second stage interferingsymbol estimates (step 2150) from the previous run. These symbolestimates, which may be conditional means, can be stored in the usermemories 460. In step 2320, the variances of the interfering symbols aredetermined based on the residual powers. Recall that the variances canbe approximated from residual powers, which are powers remaining afterthe previous stage interferences are canceled. Thus, in oneimplementation of step 2330, the variances are approximated from theresidual powers.

Referring back to FIG. 21, the equalizer 420 determines the second stagecombining weights based on the second stage impairment covariance instep 2130. Then in step 2140, the equalizer 420 performs a second stageequalization of the second composite signal based on the second stagecombining weights. The despreader/combiner 1610 embodiments illustratedin FIGS. 17A and 17B process the second composite signal much like thefirst composite signal is processed. The processed second compositesignal can be output as the second stage equalized signal. But morepreferably, both embodiments also perform add-back of soft symbolestimates of the previous stage to output a more fully equalized signalas the second equalized signal. The second combining weights can becombining weights of energy-collecting fingers only or combining weightsfor both energy-collecting and interference-suppression fingers.

FIG. 24 illustrates a flow chart of an example process to implement step2140 to perform the second stage equalization. In step 2410, the delaytiming determiner 1620 determines the second stage processing delays instep 2410. Then in step 2420, the despreader/combiner despreads andcombines the second composite signal based on the second stage combiningweights. The despread and combined values can be to output as the secondstage equalized signal at this point. But more preferably, add-back isperformed in step 2430 and the result is output as the second stageequalized signal, which can be viewed as a stream of equalized symbols.

Referring back to FIG. 21, the receiver 400 estimates one or more secondstage interferences to the symbol of interest based on the second stageequalized signal in step 2150. Again, to estimate the second stageinterferences, the interfering symbols are estimated. Any of theprocesses illustrated in FIGS. 20A, 20B and 20C may be used to estimatethe second stage interferences. The demodulation/estimation steps arerepeated for all symbols carried by signal x_(i), and in doing so, thesecond stage estimates of symbols for symbols of interest as well asestimates of interfering symbols are generated.

Based on the second stage interferences determined in step 2150, theinterference canceller 470 cancels the second stage interfering symbolestimates from the second composite signal to generate the furtherinterference-reduced version of the first composite signal in step 2160.The process illustrated in FIG. 19 may be used to implement step 2160.Note that steps 2150 and 2160 are optional.

One significant advantage of the disclosed subject matter is it allowsobtaining an updated impairment covariance matrix after soft symbolcancellation. Using the proposed method, there is no need to re-despreadthe unoccupied channelization codes in subsequent stages of turbo-ICoperation, thus avoiding much complexity. Instead, the updatedimpairment covariance matrix after soft symbol cancellation can beobtained by parametrically modifying the original impairment covariancematrix obtained using the unoccupied channelization code despreading andaveraging.

Although the description above contains many specificities, these shouldnot be construed as limiting the scope of the disclosed subject matterbut as merely providing illustrations of some of the presently preferredembodiments. Therefore, it will be appreciated that the scope of thedisclosed subject matter fully encompasses other embodiments which maybecome obvious to those skilled in the art, and that the scope isaccordingly not to be limited. All structural, and functionalequivalents to the elements of the above-described preferred embodimentthat are known to those of ordinary skill in the art are expresslyincorporated herein by reference and are intended to be encompassedhereby. Moreover, it is not necessary for a device or method to addresseach and every problem described herein or sought to be solved by thepresent technology, for it to be encompassed hereby.

What is claimed is:
 1. A method performed in a receive node of acommunication network to perform a first stage processing a symbol ofinterest contained in a first composite signal, and to perform a secondstage processing the same symbol of interest contained in a secondcomposite signal, wherein the first stage processing comprises:determining a first stage impairment covariance estimate; determiningone or more first stage combining weights based on the first stageimpairment covariance estimate; performing a first stage equalization ofthe first composite signal based on the first stage combining weights togenerate a first stage equalized signal; determining one or more firststage interfering symbol estimates based on the first stage equalizedsignal; and canceling the first stage interfering symbol estimates fromthe first composite signal to generate an interference-reduced versionof the first composite signal, and wherein the second stage processingcomprises: determining a second stage impairment covariance estimatebased on the first stage impairment covariance estimate and one or moreprevious stage interfering symbol estimates; and determining one or moresecond stage combining weights based on the second stage impairmentcovariance estimate; performing a second stage equalization of thesecond composite signal based on the second stage combining weights togenerate a second stage equalized signal, wherein the second compositesignal is based on the interference-reduced version of the firstcomposite signal, and wherein the previous stage corresponds to thefirst stage processing or to a previous run of the second stageprocessing.
 2. The method of claim 1, wherein in the step of determiningthe first stage impairment covariance estimate, the first stageimpairment covariance estimate is determined based on unoccupiedchannelization code despreading and averaging.
 3. The method of claim 1,wherein the step of performing the first stage equalization of the firstcomposite signal comprises: determining first stage processing delays;and despreading and combining the first composite signal based on thefirst stage processing delays and the first stage combining weights togenerate the first stage equalized signal.
 4. The method of claim 1,wherein the second stage processing further comprises determining one ormore second stage interfering symbol estimates based on the second stageequalized signal, wherein the step of determining the second stageimpairment covariance estimate comprises: determining variances ofinterfering symbols based on the previous stage interfering symbolestimates; and updating the first stage impairment covariance estimatebased on the variances of the interfering symbols, the updated firststage impairment covariance estimate representing the second stageimpairment covariance estimate, and wherein the previous stageinterfering symbol estimates are the first stage interfering symbolestimates from the first stage processing or the second stageinterfering symbol estimates from a previous run of the second stageprocessing.
 5. The method of claim 4, wherein in the step of determiningthe variances of interfering symbols, the variances of interferingsymbols are obtained based on residual interference power levels whichare power levels of corresponding to the interfering symbols that remainafter interference is canceled from a previous composite signal, andwherein the previous composite signal is the first composite signal orthe second composite signal in the previous run of the second stageprocessing.
 6. The method of claim 5, wherein the residual interferencepower level(s) are obtained through computing average power values ofthe previous stage interfering symbol estimates of the interferingsymbols regenerated using likelihood indicators, wherein each likelihoodindicator relates to an estimate of a corresponding interfering symbol.7. The method of claim 6, wherein each likelihood indicator is any oneor more of bit log-likelihood ratios (LLR), bit likelihood ratios, andbit probabilities of the interfering symbol corresponding to thatlikelihood indicator.
 8. The method of claim 4, wherein the step ofdetermining the first stage interfering symbol estimates comprises:demodulating the first stage equalized signal to generate first stagedemodulated bit soft values; soft decoding the first stage demodulatedbit soft, values to generate first stage likelihood indicators; anddetermining the first stage interfering symbol estimates based on thefirst stage likelihood indicators.
 9. The method of claim 4, wherein thestep of determining the first stage interfering symbol estimatescomprises: demodulating the first stage equalized signal to generatefirst stage demodulated bits or first stage likelihood indicators; anddetermining the first stage interfering symbol estimates based on thefirst stage demodulated bits or the first stage likelihood indicators,and wherein the step of determining the second stage interfering symbolestimates comprises: demodulating the second stage equalized signal togenerate second stage demodulated bits or second stage likelihoodindicators; and determining the second stage interference interferingsymbol estimates based on the second stage demodulated bits or thesecond stage likelihood indicators.
 10. The method of claim 4, whereinthe step of determining the first stage interfering symbol estimatescomprises: demodulating the first stage equalized signal to generatefirst stage demodulated bits or first stage likelihood indicators;decoding the first stage demodulated bits or first stage likelihoodindicators to generate first stage decoded information bit values;reencoding the first stage decoded information bit values to generatefirst stage reencoded bit values; and determining the first stageinterfering symbol estimates based on the first stage reencoded bitvalues.
 11. The method of claim 4, wherein the second stage processingfurther comprises canceling the second stage interfering symbolestimates from the second composite signal to generate furtherinterference-reduced version of the first composite signal.
 12. Themethod of claim 11, wherein the step of canceling the first stageinterfering symbol estimates comprises: determining a first stage signalestimate based on the first stage interfering symbol estimates; andcanceling the first stage signal estimate from the first compositesignal, and wherein the step of canceling the second stage interferingsymbol estimates comprises: determining a second stage signal estimatebased on the second stage interfering symbol estimates; and cancelingthe second stage signal estimate from the second composite signal. 13.The method of claim 4, wherein the second stage combining weights aredetermined based on the second stage impairment covariance estimate inthe step of determining the second stage combining weights.
 14. Themethod of claim 4, wherein the previous stage interfering symbolestimates comprise hard and soft previous stage interfering symbolestimates, wherein in the previous stage, interference is canceled basedon one of the hard and soft previous stage interfering symbol estimates,and wherein the second stage impairment covariance estimate isdetermined by updating the first stage impairment covariance estimatebased on the other of the hard and soft previous stage interferingsymbol estimates.
 15. The method of claim 4, wherein the previous stageinterfering symbol estimates comprise pre-decode and post-decodeprevious stage interfering symbol estimates, wherein in the previousstage, interference is canceled based on one of the pre-decode andpost-decode previous stage interfering symbol estimates, and wherein thesecond stage impairment covariance estimate is determined by updatingthe first stage impairment covariance estimate based on the other of thepre-decode and post-decode previous stage interfering symbol estimates.16. The method of claim 4, wherein the previous stage interfering symbolestimates comprise pre-decode and post-decode previous stage interferingsymbol estimates, and wherein the second stage impairment covarianceestimate corresponding to a data channel is determined by updating thefirst stage impairment covariance estimate based on the post-decodeprevious stage interfering symbol estimates corresponding to the datachannel.
 17. A receiver of a receive node of a communication networkcomprising a plurality of chains, each chain structured to process asymbol of interest contained in a first composite signal in a firststage, and to process the same symbol of interest contained in a secondcomposite signal in a second stage, each chain of the receivercomprising: an equalizer; a demodulator; a signal regenerator; and aninterference canceller, wherein in the first stage, the equalizer isstructured to determine a first stage impairment covariance estimate, todetermine one or more first stage combining weights based on the firststage impairment covariance estimate, and to perform a first stageequalization of the first composite signal based on the first stagecombining weights to generate a first stage equalized signal, thedemodulator is structured to demodulate the first equalized signal togenerate a first stage demodulated data, the signal regenerator isstructured to determine one or more first stage interfering symbolestimates based on the first stage demodulated data, and theinterference canceller is structured to cancel the first stageinterfering symbol estimates from the first composite signal to generatean interference-reduced version of the first composite signal, whereinin the second stage, the equalizer is structured to determine a secondstage impairment covariance estimate based on the first stage impairmentcovariance estimate and one or more previous stage interfering symbolestimates, to determine one or more second stage combining weights basedon the second stage impairment covariance estimate, and to perform asecond stage equalization of the second composite signal based on thesecond stage combining weights to generate a second stage equalizedsignal, wherein the second composite signal is based on theinterference-reduced version of the first composite signal, and whereinthe previous stage corresponds to the first stage or to a previous runof the second stage.
 18. The receiver of claim 17, wherein the equalizeris structured to determine a first stage impairment covariance estimatebased on unoccupied channelization code despreading and averaging. 19.The receiver of claim 17, wherein the equalizer is structured todetermine the first stage processing delays, and despread and combinethe first composite signal based on the first stage processing delaysand the first stage combining weights to generate the first stageequalized signal.
 20. The receiver of claim 17, wherein the signalregenerator is structured to determine one or more second stageinterfering symbol estimates based on the second stage equalized signal,wherein the equalizer is structured to determine variances ofinterfering symbols based on the previous stage interfering symbolestimates, and to update the first stage impairment covariance estimatebased on the variances of the interfering symbols, the updated firststage impairment covariance estimate representing the second stageimpairment covariance estimate, and wherein the previous stageinterfering symbol estimates are the first stage interfering symbolestimates from the first stage or the second stage interfering symbolestimates from a previous run of the second stage.
 21. The receiver ofclaim 20, wherein the equalizer is structured to determine the variancesof interfering symbols based on residual interference power levels whichare power levels of corresponding to the interfering symbols that remainafter interference is canceled from a previous composite signal, andwherein the previous composite signal is the first composite signal orthe second composite signal in the previous run of the second stageprocessing.
 22. The receiver of claim 21, wherein the equalizer isstructured to determine the residual interference power level throughcomputing average power values of the previous stage interfering symbolestimates of the interfering symbols regenerated using likelihoodindicators, wherein each likelihood indicator relates to an estimate ofa corresponding interfering symbol.
 23. The receiver of claim 22,wherein each likelihood indicator is any one or more of bitlog-likelihood ratios (LLR), bit likelihood ratios, and bitprobabilities of the interfering symbol corresponding to that likelihoodindicator.
 24. The receiver of claim 20, further comprising a decoder,wherein the demodulator is structured to demodulate the first stageequalized signal to generate first stage demodulated bit soft values,wherein the decoder is structured to soft decode the first stagedemodulated bit soft values to generate first stage likelihoodindicators, and wherein the signal regenerator is structured to generatethe first stage interfering symbol estimates based on the first stagelikelihood indicators.
 25. The receiver of claim 20, wherein thedemodulator is structured to demodulate the first stage equalized signalto generate first stage demodulated bits or first stage likelihoodindicators, and structured to demodulate the second stage equalizedsignal to generate second stage demodulated bits or second stagelikelihood indicators, and wherein the signal regenerator is structuredto generate the first stage interfering symbol estimates based on thefirst stage demodulated bits or the first stage likelihood indicatorsand structured to generate the second stage interfering symbol estimatesbased on the second stage demodulated bits or second stage likelihoodindicators.
 26. The receiver of claim 20, further comprising a decoder,wherein the demodulator is structured to demodulate the first stageequalized signal to generate first stage demodulated bits or first stagelikelihood indicators, wherein the decoder is structured to decode thefirst stage demodulated bits or the first stage likelihood indicators togenerate first stage decoded bit hard values, and wherein the signalregenerator is structured to reencode the first stage decoded bit hardvalues to generate first stage reencoded bit values, and to determinethe first stage interfering symbol estimates based on the first stagereencoded bit values.
 27. The receiver of claim 20, wherein in thesecond stage, the interference canceller is structured to cancel thesecond stage interfering symbol estimates from the second compositesignal to generate further interference-reduced version of the firstcomposite signal.
 28. The receiver of claim 27, wherein the signalregenerator is structured to determine a first stage signal estimatebased on the first stage interfering symbol estimates, and structured todetermine a second stage signal estimate based on the second stageinterfering symbol estimates, and wherein the interference canceller isstructured to cancel the first stage signal estimate from the firstcomposite signal, and structured to cancel the second stage signalestimate from the second composite signal.
 29. The receiver of claim 20,wherein the equalizer is structured to determine the second stagecombining weights based on the second stage impairment covarianceestimate.
 30. The receiver of claim 20, wherein the previous stageinterfering symbol estimates comprise hard and soft previous stageinterfering symbol estimates, wherein in the previous stage, theinterference canceller is structured to cancel interference based on oneof the hard and soft previous stage interfering symbol estimates, andwherein the equalizer is structured to update the first stage impairmentcovariance estimate based on the other of the hard and soft previousstage interfering symbol estimates as the second stage impairmentcovariance estimate.
 31. The receiver of claim 20, wherein the previousstage interfering symbol estimates comprise pre-decode and post-decodeprevious stage interfering symbol estimates, wherein in the previousstage, the interference canceller is structured to cancel interferencebased on one of the pre-decode and post-decode previous stageinterfering symbol estimates, and wherein the equalizer is structured toupdate the first stage impairment covariance estimate based on the otherof the pre-decode and post-decode previous stage interfering symbolestimates as the second stage impairment covariance estimate.
 32. Thereceiver of claim 20, wherein the previous stage interfering symbolestimates comprise pre-decode and post-decode previous stage interferingsymbol estimates, wherein the equalizer is structured to update thefirst stage impairment covariance estimate based on the post-decodeprevious stage interfering symbol estimates corresponding to the datachannel as the second stage impairment covariance estimate.
 33. Anon-transitory computer readable medium containing therein a programexecutable by a computer in a receive node of a communication network,when executed, the program causing the computer to perform a first stageprocessing a symbol of interest contained in a first composite signal,and to perform a second stage processing the same symbol of interestcontained in a second composite signal, wherein the first stageprocessing comprises: determining a first stage impairment covarianceestimate; determining one or more first stage combining weights based onthe first stage impairment covariance estimate; performing a first stageequalization of the first composite signal based on the first stagecombining weights to generate a first stage equalized signal;determining one or more first stage interfering symbol estimates basedon the first stage equalized signal; and canceling the first stageinterfering symbol estimates from the first composite signal to generatean interference-reduced version of the first composite signal, andwherein the second stage processing comprises: determining a secondstage impairment covariance estimate based on the first stage impairmentcovariance estimate and one or more previous stage interfering symbolestimates; and determining one or more second stage combining weightsbased on the second stage impairment covariance estimate; performing asecond stage equalization of the second composite signal based on thesecond stage combining weights to generate a second stage equalizedsignal, wherein the second composite signal is based on theinterference-reduced version of the first composite signal, and whereinthe previous stage corresponds to the first stage processing or to aprevious run of the second stage processing.
 34. The method of claim 8,wherein the step of determining the second stage interfering symbolestimates comprises: demodulating the second stage equalized signal togenerate second stage demodulated bit soft values; soft decoding thesecond stage demodulated bit soft values to generate first stagelikelihood indicators; and determining the second stage interferingsymbol estimates based on the first stage likelihood indicators.
 35. Themethod of claim 10, wherein the step of determining the second stageinterfering symbol estimates comprises: demodulating the second stageequalized signal to generate second stage demodulated bit or secondstage likelihood indicators; decoding the second stage demodulated bitsor second stage likelihood indicators to generate second stage decodedinformation bit values; reencoding the second stage decoded informationbit values to generate second stage reencoded bit values; anddetermining the second stage interfering symbol estimates based on thesecond stage reencoded bit values.
 36. The receiver of claim 24, whereinthe demodulator is structured to demodulate the second stage equalizedsignal to generate second stage demodulated bit soft values, wherein thedecoder is structured to soft decode the second stage demodulated bitsoft values to generate second stage likelihood indicators, and whereinthe signal regenerator is structured to generate the second stageinterfering symbol estimates based on the second stage likelihoodindicators.
 37. The receiver of claim 26, wherein the demodulator isstructured to demodulate the second stage equalized signal to generatesecond stage demodulated bits or second stage likelihood indicators,wherein the decoder is structured to decode the second stage demodulatedbits or the second stage likelihood indicators to generate second stagedecoded information bit values, and wherein the signal regenerator isstructured to reencode the second stage decoded information bit valuesto generate second stage reencoded bit values, and to determine thesecond stage interfering symbol estimates based on the second stagereencoded bit values.